Data-intelligence driven methods for durability, damage diagnosis and performance prediction of concrete structures
Abstract A large number of in-service reinforced concrete structures are now entering the mid-to-late stages of their service life. Efficient detection of damage characteristics and accurate prediction of material performance degradation have become essential for ensuring the safety of these structu...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-06-01
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| Series: | Communications Engineering |
| Online Access: | https://doi.org/10.1038/s44172-025-00431-4 |
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| _version_ | 1850224274373083136 |
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| author | Fan Li Daming Luo Ditao Niu |
| author_facet | Fan Li Daming Luo Ditao Niu |
| author_sort | Fan Li |
| collection | DOAJ |
| description | Abstract A large number of in-service reinforced concrete structures are now entering the mid-to-late stages of their service life. Efficient detection of damage characteristics and accurate prediction of material performance degradation have become essential for ensuring the safety of these structures. Traditional damage detection methods, which primarily rely on manual inspections and sensor monitoring, are inefficient and lack accuracy. Similarly, performance prediction models for reinforced concrete materials, which are often based on limited experimental data and polynomial fitting, oversimplify the influencing factors. In contrast, partial differential equation models that account for degradation mechanisms are computationally intensive and difficult to solve. Recent advancements in deep learning and machine learning, as part of artificial intelligence, have introduced innovative approaches for both damage detection and material performance prediction in reinforced concrete structures. This paper provides a comprehensive overview of machine learning and deep learning theories and models, and reviews the current research on their application to the durability of reinforced concrete structures, focusing on two main areas: intelligent damage detection and predictive modeling of material durability. Finally, the article discusses future trends and offers insights into the intelligent innovation of concrete structure durability. |
| format | Article |
| id | doaj-art-63f0a93bcf9f403c9e580418f89d4e00 |
| institution | OA Journals |
| issn | 2731-3395 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Engineering |
| spelling | doaj-art-63f0a93bcf9f403c9e580418f89d4e002025-08-20T02:05:41ZengNature PortfolioCommunications Engineering2731-33952025-06-014111910.1038/s44172-025-00431-4Data-intelligence driven methods for durability, damage diagnosis and performance prediction of concrete structuresFan Li0Daming Luo1Ditao Niu2State Key Laboratory of Green Building, Xi’an University of Architecture and TechnologyState Key Laboratory of Green Building, Xi’an University of Architecture and TechnologyState Key Laboratory of Green Building, Xi’an University of Architecture and TechnologyAbstract A large number of in-service reinforced concrete structures are now entering the mid-to-late stages of their service life. Efficient detection of damage characteristics and accurate prediction of material performance degradation have become essential for ensuring the safety of these structures. Traditional damage detection methods, which primarily rely on manual inspections and sensor monitoring, are inefficient and lack accuracy. Similarly, performance prediction models for reinforced concrete materials, which are often based on limited experimental data and polynomial fitting, oversimplify the influencing factors. In contrast, partial differential equation models that account for degradation mechanisms are computationally intensive and difficult to solve. Recent advancements in deep learning and machine learning, as part of artificial intelligence, have introduced innovative approaches for both damage detection and material performance prediction in reinforced concrete structures. This paper provides a comprehensive overview of machine learning and deep learning theories and models, and reviews the current research on their application to the durability of reinforced concrete structures, focusing on two main areas: intelligent damage detection and predictive modeling of material durability. Finally, the article discusses future trends and offers insights into the intelligent innovation of concrete structure durability.https://doi.org/10.1038/s44172-025-00431-4 |
| spellingShingle | Fan Li Daming Luo Ditao Niu Data-intelligence driven methods for durability, damage diagnosis and performance prediction of concrete structures Communications Engineering |
| title | Data-intelligence driven methods for durability, damage diagnosis and performance prediction of concrete structures |
| title_full | Data-intelligence driven methods for durability, damage diagnosis and performance prediction of concrete structures |
| title_fullStr | Data-intelligence driven methods for durability, damage diagnosis and performance prediction of concrete structures |
| title_full_unstemmed | Data-intelligence driven methods for durability, damage diagnosis and performance prediction of concrete structures |
| title_short | Data-intelligence driven methods for durability, damage diagnosis and performance prediction of concrete structures |
| title_sort | data intelligence driven methods for durability damage diagnosis and performance prediction of concrete structures |
| url | https://doi.org/10.1038/s44172-025-00431-4 |
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